自适应动态重组多目标粒子群优化算法  

Adaptive dynamic reconfiguration multi-objective particle swarm optimization algorithm

作　　者：倪红梅[1,2] 刘永建[1] 李盼池[2] 

机构地区：[1]东北石油大学提高油气采收率教育部重点实验室,黑龙江大庆163318 [2]东北石油大学计算机与信息技术学院,黑龙江大庆163318

出　　处：《控制与决策》2015年第8期1417-1422,共6页Control and Decision

基　　金：国家科技重大专项课题(2011ZX05012-003);国家自然科学基金项目(61170132);黑龙江省教育厅科学技术研究项目(12521058)

摘　　要：提出一种自适应动态重组粒子群优化算法.该算法采用凝聚的层次聚类算法,将种群分成若干个子群体,用一个精英集对非支配解进行存储;根据贡献度和多样性,对各子群体的粒子和整个种群进行自适应动态重组;同时引入扰动算子对精英集存储的非支配解进行扰动,实现对精英集进行动态调整.利用具有不同特点的测试函数进行验证并与同类算法相比较,结果表明,所提出的算法可加快收敛速度,提高种群的可进化能力.The adaptive dynamic reconfiguration multi-objective particle swarm optimization algorithm is proposed, which uses the agglomerate hierarchical clustering algorithm to divide the population into several groups, and stores the nondominated solutions with an elite set. According to the contribution and diversity, the particles of various subgroups and the entire population are reconfigured adaptively and dynamically. At the same time, a perturbed operator is introduced to disturb non-dominated solutions set stored in the elitist set, achieving dynamically adjustment to the elite set. Test functions with different characteristics are used to verify the proposed algorithm which is compared with other similar algorithms.The results show that the proposed algorithm can speed up the convergence speed and increase the evolving ability of the population.

关 键 词：多目标优化 动态重组 粒子群优化 精英集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]